Report last updated Thu May 26 18:20:12 2016.

Exploratory analysis

In this section we will see descriptive figures about quality of the data, reads with adapter, reads mapped to miRNAs, reads mapped to other small RNAs.

size distribution

After adapter removal, we can plot the size distribution of the small RNAs.

miRNA

total miRNA expression annotated with mirbase

Distribution of mirna expression

cumulative distribution of miRNAs

Clustering

MDS plot

complexity

Number of miRNAs with > 3 counts.

colSums(counts > 10)
normal_1 407
normal_2 449
normal_3 438
day3_1 528
day3_2 522
day3_3 514
day3_4 528
day7_1 525
day7_2 473
day7_3 528
day7_4 525
day14_1 496
day14_2 487
day14_3 530
day14_4 469

novel miRNA by mirdeep2

total miRNA expression

Distribution of mirna expression

cumulative distribution of miRNAs

Clustering

MDS plot

complexity

Number of miRNAs with > 3 counts.

colSums(counts > 10)
normal_1 132
normal_2 165
normal_3 141
day3_1 206
day3_2 199
day3_3 190
day3_4 193
day7_1 186
day7_2 139
day7_3 202
day7_4 213
day14_1 177
day14_2 161
day14_3 181
day14_4 159

Others small RNA

The data was analyzed with seqcluster

This tools used all reads, uniquely mapped and multi-mapped reads. The first step is to cluster sequences in all locations they overlap. The second step is to create meta-clusters: is the unit that merge all clusters that share the same sequences. This way the output are meta-clusters, common sequences that could come from different region of the genome.

genome covered

In this table 1 means % of the genome with at least 1 read, and 0 means % of the genome without reads.

The normal value for human data with strong small RNA signal is: 0.0002. This will change for smaller genomes.

classification

Number of reads in the data after each step:

  • raw: initial reads
  • cluster: after cluster detection
  • multimap: after meta-cluster detection

Check complex meta-clusters: This kind of events happen when there are small RNA over the whole genome, and all repetitive small rnas map to thousands of places and sharing many sequences in many positions. If any meta-cluster is > 40% of the total data, maybe it is worth to add some filters like: minimum number of counts -e or --min--shared in seqcluster prepare

     normal_1 normal_2 normal_3 day3_1 day3_2 day3_3 day3_4 day7_1 day7_2
     day7_3 day7_4 day14_1 day14_2 day14_3 day14_4

complexity

Number of miRNAs with > 10 counts.

colSums(clus_ma > 10)
normal_1 1088
normal_2 1112
normal_3 1151
day3_1 955
day3_2 910
day3_3 924
day3_4 950
day7_1 939
day7_2 843
day7_3 947
day7_4 955
day14_1 886
day14_2 857
day14_3 902
day14_4 868

Contribution by class

Differential expression

DESeq2 is used for this analysis.

Analysis for miRNA

## Comparison: uuo_model_mirna {.tabset}


out of 820 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 277, 34%
LFC < 0 (down) : 213, 26%
outliers [1] : 0, 0%
low counts [2] : 64, 7.8%
(mean count < 1)
[1] see ‘cooksCutoff’ argument of ?results
[2] see ‘independentFiltering’ argument of ?results

NULL

Differential expression file at: uuo_model_mirna.tsv

Normalized counts matrix file at: uuo_model_mirna_log2_counts.tsv

MA plot plot

Volcano plot

QC for DE genes p-values/variance

Most significand, FDR< 0.01 and log2FC > 2 : 143

Plots most significand

Plot top 9 genes

Top DE genes

baseMean log2FoldChange lfcSE stat pvalue padj symbol description day14vsnormal day3vsnormal day7vsnormal absMaxLog2FC
mmu-let-7j 17196.7569 16.663141 1.1914997 1373.2439 0 0 mmu-let-7j mmu-let-7j 16.663141 15.1755078 16.0671948 16.663141
mmu-miR-214-3p 4509.3090 3.483580 0.1334129 577.8484 0 0 mmu-miR-214-3p mmu-miR-214-3p 3.483580 2.1126894 2.5885849 3.483580
mmu-miR-199a-5p 11965.7945 2.829548 0.1250826 486.8317 0 0 mmu-miR-199a-5p mmu-miR-199a-5p 2.829548 1.3065738 2.0039423 2.829548
mmu-miR-342-3p 2547.2734 2.318403 0.1207087 400.6840 0 0 mmu-miR-342-3p mmu-miR-342-3p 2.318403 0.9240673 1.7558626 2.318403
mmu-miR-214-5p 758.1736 2.928867 0.1521295 375.3506 0 0 mmu-miR-214-5p mmu-miR-214-5p 2.928867 1.6447636 2.1729717 2.928867
mmu-miR-181d-5p 3528.5883 2.063578 0.1233060 358.8383 0 0 mmu-miR-181d-5p mmu-miR-181d-5p 2.063578 0.4108037 1.2649226 2.063578
mmu-miR-181c-3p 1848.2572 2.423765 0.1393199 353.7906 0 0 mmu-miR-181c-3p mmu-miR-181c-3p 2.423765 0.7322030 1.5962136 2.423765
mmu-miR-181c-5p 21419.0396 1.554807 0.1123914 324.2849 0 0 mmu-miR-181c-5p mmu-miR-181c-5p 1.554807 -0.0526929 0.4538337 1.554807
mmu-miR-3470b 142.6975 -4.523612 0.3598014 317.6987 0 0 mmu-miR-3470b mmu-miR-3470b -4.523612 -4.5102178 -4.5553716 4.555372
mmu-miR-223-3p 2639.0893 2.425866 0.1389933 313.1421 0 0 mmu-miR-223-3p mmu-miR-223-3p 2.425866 2.4669631 2.3902838 2.466963
mmu-miR-690 136.5074 -3.463347 0.2886409 308.0918 0 0 mmu-miR-690 mmu-miR-690 -3.463347 -4.0800284 -3.3942129 4.080028
mmu-miR-199a-3p 62983.6576 2.057319 0.1256834 306.3984 0 0 mmu-miR-199a-3p mmu-miR-199a-3p 2.057319 0.5826449 1.1687094 2.057319
mmu-miR-199b-3p 62890.2352 2.055937 0.1256859 306.2051 0 0 mmu-miR-199b-3p mmu-miR-199b-3p 2.055937 0.5808003 1.1672925 2.055937
mmu-miR-107-3p 14684.1912 -2.069082 0.1243097 290.5250 0 0 mmu-miR-107-3p mmu-miR-107-3p -2.069082 -1.0066671 -1.2550736 2.069082
mmu-miR-203-3p 6319.8042 -1.570119 0.1046553 287.8683 0 0 mmu-miR-203-3p mmu-miR-203-3p -1.570119 -1.2104244 -1.2820482 1.570119
mmu-miR-298-5p 609.5362 4.488035 0.2626671 286.2279 0 0 mmu-miR-298-5p mmu-miR-298-5p 4.488035 4.0705497 4.2217573 4.488035
mmu-miR-21a-5p 611714.0599 2.407867 0.1470566 260.3862 0 0 mmu-miR-21a-5p mmu-miR-21a-5p 2.407867 2.4767575 2.1719989 2.476758
mmu-miR-192-5p 748475.3200 -2.245289 0.1447797 245.8973 0 0 mmu-miR-192-5p mmu-miR-192-5p -2.245289 -0.9393249 -1.2570751 2.245289
mmu-miR-6481 18.4137 -8.999630 1.1344258 243.9030 0 0 mmu-miR-6481 mmu-miR-6481 -8.999630 -9.3276306 -7.7306283 9.327631
mmu-miR-375-3p 8096.4913 2.308064 0.1580264 237.6600 0 0 mmu-miR-375-3p mmu-miR-375-3p 2.308064 1.4975624 2.4620554 2.462055


Working with  143  genes 

Analysis for novel miRNA

## Comparison: uuo_model_novel {.tabset}


out of 663 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 97, 15%
LFC < 0 (down) : 75, 11%
outliers [1] : 1, 0.15%
low counts [2] : 206, 31%
(mean count < 2)
[1] see ‘cooksCutoff’ argument of ?results
[2] see ‘independentFiltering’ argument of ?results

NULL

Differential expression file at: uuo_model_novel.tsv

Normalized counts matrix file at: uuo_model_novel_log2_counts.tsv

MA plot plot

Volcano plot

QC for DE genes p-values/variance

Most significand, FDR< 0.01 and log2FC > 2 : 54

Plots most significand

Plot top 9 genes

Top DE genes

baseMean log2FoldChange lfcSE stat pvalue padj symbol description day14vsnormal day3vsnormal day7vsnormal absMaxLog2FC
mmu-chr2_3967-3p 12455.44084 2.868397 0.1523810 342.90127 0 0 mmu-chr2_3967-3p mmu-chr2_3967-3p 2.868397 1.2491747 2.0151922 2.868397
mmu-chr14_36947-5p 34500.15547 -1.267335 0.0816531 265.41648 0 0 mmu-chr14_36947-5p mmu-chr14_36947-5p -1.267335 -0.6353119 -0.8992341 1.267335
mmu-chr2_3967-5p 63906.55197 2.148616 0.1511644 237.37529 0 0 mmu-chr2_3967-5p mmu-chr2_3967-5p 2.148616 0.5480982 1.2219024 2.148616
mmu-chr1_1710-5p 61229.68982 -2.838724 0.1853797 234.18720 0 0 mmu-chr1_1710-5p mmu-chr1_1710-5p -2.838724 -1.1702170 -1.5254434 2.838724
mmu-chr10_27534-5p 291.20195 -2.661631 0.3799245 169.08330 0 0 mmu-chr10_27534-5p mmu-chr10_27534-5p -2.661631 -4.4536825 -3.4713018 4.453683
mmu-chr12_33228-5p 394.46649 -3.920365 0.3119880 162.74456 0 0 mmu-chr12_33228-5p mmu-chr12_33228-5p -3.920365 -1.5040657 -2.2821055 3.920365
mmu-chr11_29159-5p 1485.95730 -1.942999 0.1637482 142.78845 0 0 mmu-chr11_29159-5p mmu-chr11_29159-5p -1.942999 -0.8219266 -1.0187588 1.942999
mmu-chr18_44409-3p 25.29838 -5.774959 0.7101346 141.30836 0 0 mmu-chr18_44409-3p mmu-chr18_44409-3p -5.774959 -4.0668396 -4.7988539 5.774959
mmu-chr9_24522-5p 218.06824 1.603979 0.1540218 141.23435 0 0 mmu-chr9_24522-5p mmu-chr9_24522-5p 1.603979 0.6199801 0.7091580 1.603979
mmu-chr2_3669-5p 80.94701 3.482811 0.3754897 115.98403 0 0 mmu-chr2_3669-5p mmu-chr2_3669-5p 3.482811 2.8708320 3.0499733 3.482811
mmu-chr9_25407-5p 146.32194 -2.876766 0.4106088 115.43266 0 0 mmu-chr9_25407-5p mmu-chr9_25407-5p -2.876766 -3.5303319 -3.2390911 3.530332
mmu-chr8_22923-5p 208.99375 -2.359552 0.2289875 110.24059 0 0 mmu-chr8_22923-5p mmu-chr8_22923-5p -2.359552 -0.9125601 -1.2629244 2.359552
mmu-chr3_7265-5p 800.64810 -1.881034 0.1805992 109.89981 0 0 mmu-chr3_7265-5p mmu-chr3_7265-5p -1.881034 -0.9076219 -0.9742646 1.881034
mmu-chr19_45110-5p 19574.71003 3.215253 0.2883166 108.18560 0 0 mmu-chr19_45110-5p mmu-chr19_45110-5p 3.215253 2.1646113 3.0408238 3.215253
mmu-chr4_12013-5p 1906.41267 2.916561 0.2900350 107.55329 0 0 mmu-chr4_12013-5p mmu-chr4_12013-5p 2.916561 1.2634602 1.2607801 2.916561
mmu-chr4_11054-5p 361.06471 -1.246918 0.1313939 93.41984 0 0 mmu-chr4_11054-5p mmu-chr4_11054-5p -1.246918 -0.7336877 -0.7576263 1.246918
mmu-chr11_29516-5p 13.02515 -3.617903 0.5262521 92.60477 0 0 mmu-chr11_29516-5p mmu-chr11_29516-5p -3.617903 -3.0586854 -3.3761890 3.617903
mmu-chr11_30090-5p 12727.01028 1.260855 0.2139739 91.81354 0 0 mmu-chr11_30090-5p mmu-chr11_30090-5p 1.260855 -0.5113367 0.8747116 1.260855
mmu-chr3_7309-5p 38.18248 -2.791493 0.4458375 89.26606 0 0 mmu-chr3_7309-5p mmu-chr3_7309-5p -2.791493 -3.2314050 -3.2446290 3.244629
mmu-chr2_3666-5p 72.55036 2.358017 0.2878779 85.91712 0 0 mmu-chr2_3666-5p mmu-chr2_3666-5p 2.358017 1.3139918 1.8279643 2.358017


Working with  54  genes 

Analysis for isomiRs

## Comparison: uuo_model_iso {.tabset}


out of 15556 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2867, 18%
LFC < 0 (down) : 1940, 12%
outliers [1] : 44, 0.28%
low counts [2] : 3921, 25%
(mean count < 1)
[1] see ‘cooksCutoff’ argument of ?results
[2] see ‘independentFiltering’ argument of ?results

NULL

Differential expression file at: uuo_model_iso.tsv

Normalized counts matrix file at: uuo_model_iso_log2_counts.tsv

MA plot plot

Volcano plot

QC for DE genes p-values/variance

Most significand, FDR< 0.01 and log2FC > 2 : 1255

Plots most significand

Plot top 9 genes

Top DE genes

baseMean log2FoldChange lfcSE stat pvalue padj symbol description day14vsnormal day3vsnormal day7vsnormal absMaxLog2FC
mmu-let-7j.iso.t5:0.t3:t.ad:0.mm:8GT 15830.51671 16.535456 1.1920295 1302.3392 0 0 mmu-let-7j.iso.t5:0.t3:t.ad:0.mm:8GT mmu-let-7j.iso.t5:0.t3:t.ad:0.mm:8GT 16.535456 15.2862969 16.068747 16.535456
mmu-let-7j.iso.t5:0.t3:0.ad:0.mm:8GT 649.56593 12.000180 1.1889416 772.7313 0 0 mmu-let-7j.iso.t5:0.t3:0.ad:0.mm:8GT mmu-let-7j.iso.t5:0.t3:0.ad:0.mm:8GT 12.000180 10.4386561 11.485399 12.000180
mmu-miR-199a-5p.iso.t5:0.t3:c.ad:T.mm:0 1306.92994 2.639637 0.1111085 660.7248 0 0 mmu-miR-199a-5p.iso.t5:0.t3:c.ad:T.mm:0 mmu-miR-199a-5p.iso.t5:0.t3:c.ad:T.mm:0 2.639637 1.0103092 1.923061 2.639637
mmu-miR-199a-5p.iso.t5:0.t3:0.ad:T.mm:0 2822.02184 2.987341 0.1210492 597.1927 0 0 mmu-miR-199a-5p.iso.t5:0.t3:0.ad:T.mm:0 mmu-miR-199a-5p.iso.t5:0.t3:0.ad:T.mm:0 2.987341 1.4808757 2.342853 2.987341
mmu-miR-199a-5p.iso.t5:0.t3:c.ad:A.mm:0 1346.55052 3.081465 0.1289157 596.3583 0 0 mmu-miR-199a-5p.iso.t5:0.t3:c.ad:A.mm:0 mmu-miR-199a-5p.iso.t5:0.t3:c.ad:A.mm:0 3.081465 1.4728201 2.240962 3.081465
mmu-miR-199a-3p.iso.t5:a.t3:0.ad:A.mm:0 329.26777 2.889891 0.1490949 591.1972 0 0 mmu-miR-199a-3p.iso.t5:a.t3:0.ad:A.mm:0 mmu-miR-199a-3p.iso.t5:a.t3:0.ad:A.mm:0 2.889891 0.9875019 1.818187 2.889891
mmu-miR-199b-3p.iso.t5:a.t3:0.ad:A.mm:0 329.26777 2.889891 0.1490949 591.1972 0 0 mmu-miR-199b-3p.iso.t5:a.t3:0.ad:A.mm:0 mmu-miR-199b-3p.iso.t5:a.t3:0.ad:A.mm:0 2.889891 0.9875019 1.818187 2.889891
mmu-miR-214-3p.iso.t5:T.t3:0.ad:0.mm:0 616.38248 3.348702 0.1575864 471.8761 0 0 mmu-miR-214-3p.iso.t5:T.t3:0.ad:0.mm:0 mmu-miR-214-3p.iso.t5:T.t3:0.ad:0.mm:0 3.348702 2.4790523 2.843752 3.348702
mmu-miR-199a-5p.ref.t5:0.t3:c.ad:0.mm:0 1230.41137 2.633604 0.1250568 460.2695 0 0 mmu-miR-199a-5p.ref.t5:0.t3:c.ad:0.mm:0 mmu-miR-199a-5p.ref.t5:0.t3:c.ad:0.mm:0 2.633604 1.2636393 1.897398 2.633604
mmu-miR-21a-5p.ref.t5:0.t3:a.ad:0.mm:0 39909.99101 2.181204 0.1023021 442.1807 0 0 mmu-miR-21a-5p.ref.t5:0.t3:a.ad:0.mm:0 mmu-miR-21a-5p.ref.t5:0.t3:a.ad:0.mm:0 2.181204 1.0628462 1.669522 2.181204
mmu-let-7j.iso.t5:0.t3:0.ad:T.mm:8GT 253.41501 10.589080 1.1937498 441.2404 0 0 mmu-let-7j.iso.t5:0.t3:0.ad:T.mm:8GT mmu-let-7j.iso.t5:0.t3:0.ad:T.mm:8GT 10.589080 9.0049771 10.235234 10.589080
mmu-miR-214-3p.ref.t5:0.t3:0.ad:0.mm:0 1871.61883 3.248359 0.1521362 388.3111 0 0 mmu-miR-214-3p.ref.t5:0.t3:0.ad:0.mm:0 mmu-miR-214-3p.ref.t5:0.t3:0.ad:0.mm:0 3.248359 2.2270662 2.519238 3.248359
mmu-miR-203-3p.ref.t5:g.t3:ag.ad:0.mm:0 290.58840 -2.926500 0.1588384 387.2829 0 0 mmu-miR-203-3p.ref.t5:g.t3:ag.ad:0.mm:0 mmu-miR-203-3p.ref.t5:g.t3:ag.ad:0.mm:0 -2.926500 -1.3642636 -1.939590 2.926500
mmu-miR-3068-3p.iso.t5:gg.t3:TTC.ad:0.mm:0 87.18686 -5.251670 0.3799244 382.4183 0 0 mmu-miR-3068-3p.iso.t5:gg.t3:TTC.ad:0.mm:0 mmu-miR-3068-3p.iso.t5:gg.t3:TTC.ad:0.mm:0 -5.251670 -5.0172075 -4.495660 5.251670
mmu-miR-342-3p.ref.t5:0.t3:0.ad:0.mm:0 1115.02384 2.203405 0.1292607 374.8995 0 0 mmu-miR-342-3p.ref.t5:0.t3:0.ad:0.mm:0 mmu-miR-342-3p.ref.t5:0.t3:0.ad:0.mm:0 2.203405 0.5980105 1.650628 2.203405
mmu-miR-21a-5p.ref.t5:0.t3:0.ad:0.mm:0 240399.22745 2.382389 0.1205426 374.1056 0 0 mmu-miR-21a-5p.ref.t5:0.t3:0.ad:0.mm:0 mmu-miR-21a-5p.ref.t5:0.t3:0.ad:0.mm:0 2.382389 2.3024840 2.319410 2.382389
mmu-miR-199a-5p.ref.t5:0.t3:0.ad:0.mm:0 3065.36555 2.952847 0.1410750 369.7606 0 0 mmu-miR-199a-5p.ref.t5:0.t3:0.ad:0.mm:0 mmu-miR-199a-5p.ref.t5:0.t3:0.ad:0.mm:0 2.952847 2.0788768 2.359706 2.952847
mmu-miR-214-3p.ref.t5:0.t3:t.ad:0.mm:0 345.66007 3.438645 0.1910707 362.4505 0 0 mmu-miR-214-3p.ref.t5:0.t3:t.ad:0.mm:0 mmu-miR-214-3p.ref.t5:0.t3:t.ad:0.mm:0 3.438645 2.3870423 2.580295 3.438645
mmu-miR-21a-5p.iso.t5:0.t3:0.ad:A.mm:0 1694.76036 2.541781 0.1281267 360.4524 0 0 mmu-miR-21a-5p.iso.t5:0.t3:0.ad:A.mm:0 mmu-miR-21a-5p.iso.t5:0.t3:0.ad:A.mm:0 2.541781 1.6206227 1.983650 2.541781
mmu-miR-181c-5p.iso.t5:0.t3:T.ad:0.mm:0 6577.11394 1.993213 0.1220692 354.4688 0 0 mmu-miR-181c-5p.iso.t5:0.t3:T.ad:0.mm:0 mmu-miR-181c-5p.iso.t5:0.t3:T.ad:0.mm:0 1.993213 0.3040763 1.093341 1.993213


Working with  1255  genes 

Analysis for clusters

## Comparison: uuo_model_cluster {.tabset}


out of 1260 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 412, 33%
LFC < 0 (down) : 623, 49%
outliers [1] : 21, 1.7%
low counts [2] : 0, 0%
(mean count < 2)
[1] see ‘cooksCutoff’ argument of ?results
[2] see ‘independentFiltering’ argument of ?results

NULL

Differential expression file at: uuo_model_cluster.tsv

Normalized counts matrix file at: uuo_model_cluster_log2_counts.tsv

MA plot plot

Volcano plot

QC for DE genes p-values/variance

Most significand, FDR< 0.01 and log2FC > 2 : 411

Plots most significand

Plot top 9 genes

Top DE genes

baseMean log2FoldChange lfcSE stat pvalue padj symbol description day14vsnormal day3vsnormal day7vsnormal absMaxLog2FC
732 4144.0068 -5.003363 0.2239383 1047.2960 0 0 732 732 -5.003363 -5.5094763 -5.4222126 5.509476
753 5509.5644 3.730110 0.1236823 718.9083 0 0 753 753 3.730110 2.5975177 2.8892004 3.730110
402 2670.7099 2.666871 0.1072673 582.3122 0 0 402 402 2.666871 1.5404747 2.1616314 2.666871
549 220.9506 -8.338489 0.5906487 543.5616 0 0 549 549 -8.338489 -12.6731236 -9.7177325 12.673124
896 76649.8804 2.470146 0.1050355 512.2425 0 0 896 896 2.470146 1.2383450 1.6454122 2.470146
1020 3396.5949 2.385444 0.1103538 487.2850 0 0 1020 1020 2.385444 0.9547363 1.6091973 2.385444
1 839.0796 2.475936 0.1465858 372.8682 0 0 1 1 2.475936 2.7699874 2.7612029 2.769987
959 419710.2221 -4.288034 0.2808288 367.8342 0 0 959 959 -4.288034 -3.5737299 -3.8493541 4.288034
646 3287.8541 2.749086 0.1486909 357.4418 0 0 646 646 2.749086 3.0342532 2.7058543 3.034253
474 125218.6091 2.607244 0.1289644 341.0667 0 0 474 474 2.607244 2.0300746 2.1354353 2.607244
194 423.5401 -3.375330 0.2708094 338.1830 0 0 194 194 -3.375330 -3.6487035 -3.8166709 3.816671
805 654219.5010 2.716539 0.1506705 331.1213 0 0 805 805 2.716539 3.0396918 2.5238644 3.039692
801 802.8595 1.339980 0.1800221 329.7980 0 0 801 801 1.339980 3.0225024 1.2314627 3.022502
765 23325.9387 1.920468 0.1141773 329.2877 0 0 765 765 1.920468 0.5554461 0.8953186 1.920468
1170 649.1741 5.001559 0.2748006 324.4293 0 0 1170 1170 5.001559 4.7958967 4.7537319 5.001559
810 304.1562 -3.074237 0.2461438 319.0642 0 0 810 810 -3.074237 -3.1817807 -3.3567334 3.356733
231 8333.3827 2.619282 0.1482239 317.2094 0 0 231 231 2.619282 2.0565441 2.8148551 2.814855
764 349157.8824 1.440832 0.0962390 301.9864 0 0 764 764 1.440832 0.2119441 0.5785047 1.440832
520 14501.3767 -5.778984 0.3896322 299.1733 0 0 520 520 -5.778984 -4.1369947 -4.7743739 5.778984
332 52.0379 -10.487367 1.1404457 298.9581 0 0 332 332 -10.487367 -8.5486763 -6.9915049 10.487367


Working with  411  genes 

Files

Files generated contains raw count, normalized counts, log2 normalized counts and DESeq2 results.

R Session Info

(useful if replicating these results)

R version 3.3.0 (2016-05-03)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Debian GNU/Linux stretch/sid

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
 [1] grid      stats4    parallel  methods   stats     graphics  grDevices
 [8] utils     datasets  base     

other attached packages:
 [1] org.Mm.eg.db_3.3.0         AnnotationDbi_1.34.3      
 [3] vsn_3.40.0                 DEGreport_1.5.0           
 [5] quantreg_5.24              SparseM_1.7               
 [7] edgeR_3.14.0               limma_3.28.5              
 [9] cluster_2.0.4              pheatmap_1.0.8            
[11] isomiRs_0.99.13            DiscriMiner_0.1-29        
[13] dplyr_0.4.3                devtools_1.11.1           
[15] gridExtra_2.2.1            gtools_3.5.0              
[17] CHBUtils_0.1               genefilter_1.54.2         
[19] DESeq2_1.12.2              SummarizedExperiment_1.2.2
[21] Biobase_2.32.0             GenomicRanges_1.24.0      
[23] GenomeInfoDb_1.8.1         IRanges_2.6.0             
[25] S4Vectors_0.10.1           BiocGenerics_0.18.0       
[27] reshape_0.8.5              ggplot2_2.1.0             
[29] myRfunctions_0.1           knitr_1.13                
[31] rmarkdown_0.9.6            BiocInstaller_1.22.2      

loaded via a namespace (and not attached):
 [1] bitops_1.0-6          RColorBrewer_1.1-2    tools_3.3.0          
 [4] R6_2.1.2              affyio_1.42.0         rpart_4.1-10         
 [7] KernSmooth_2.23-15    Hmisc_3.17-4          DBI_0.4-1            
[10] lazyeval_0.1.10       colorspace_1.2-6      nnet_7.3-12          
[13] withr_1.0.1           GGally_1.0.1          Nozzle.R1_1.1-1      
[16] preprocessCore_1.34.0 chron_2.3-47          formatR_1.4          
[19] logging_0.7-103       labeling_0.3          caTools_1.17.1       
[22] scales_0.4.0          affy_1.50.0           stringr_1.0.0        
[25] digest_0.6.9          foreign_0.8-66        XVector_0.12.0       
[28] htmltools_0.3.5       highr_0.6             RSQLite_1.0.0        
[31] BiocParallel_1.6.2    acepack_1.3-3.3       magrittr_1.5         
[34] Formula_1.2-1         Matrix_1.2-6          Rcpp_0.12.5          
[37] munsell_0.4.3         stringi_1.0-1         yaml_2.1.13          
[40] zlibbioc_1.18.0       gplots_3.0.1          plyr_1.8.3           
[43] gdata_2.17.0          lattice_0.20-33       splines_3.3.0        
[46] annotate_1.50.0       locfit_1.5-9.1        geneplotter_1.50.0   
[49] codetools_0.2-14      XML_3.98-1.4          evaluate_0.9         
[52] latticeExtra_0.6-28   data.table_1.9.6      MatrixModels_0.4-1   
[55] gtable_0.2.0          tidyr_0.4.1           assertthat_0.1       
[58] xtable_1.8-2          coda_0.18-1           survival_2.39-4      
[61] memoise_1.0.0